Smart phones and other mobile devices use a variety of sensors for detecting motion and for generating estimated positions of the mobile device. Such sensors include pressure sensors, accelerometers, gyroscopes, and magnetometers. Such sensors are typically low cost and are unstable because the sensors are subject to drift over time, which results in erroneous measurements of pressure or movement that are not within a tolerated threshold value from the true pressure or movement. Drift is a phenomenon whereby the unstable sensor's measurements of circumstances deviate from the true values of those circumstances over time—e.g., the value at zero motion gradually drifts away from zero even when the sensor is not moving. The drift may be monotonic, or it may gradually change direction and return toward zero accumulated drift. Because drift cannot be predictably modeled, it is difficult to determine a correction model in advance.
Depending upon the use of an unstable sensor, drift can have significant effects. For example, if an accelerometer indicates even a slight acceleration when no movement is occurring, an application for tracking a mobile device's movement could assume the mobile device is actually moving away from the spot where the measurement started. Similarly, if measurements by a pressure sensor begin to drift away from true pressure, estimating a mobile device's altitude using an inaccurately measured pressure value will result in a significantly erroneous estimated altitude that cannot be used for emergency response or other applications. Even when unstable sensors are calibrated at the time of manufacture or at the time of installation, such sensors are still prone to drift in the field when used over time. Thus, there is a need for calibrating unstable sensors.